Efficient Unbiased Tracking of Multiple Dynamic Obstacles Under Large Viewpoint Changes

摘要

A novel-tracking algorithm is presented as a computationally feasible, real-time solution to the joint estimation problem of data assignment and dynamic obstacle tracking from a potentially moving robotic platform. The algorithm implements a Rao-Blackwellized particle filter (RBPF) to factorize the joint estimation problem into 1) a data assignment problem solved via particle filter and 2) a multiple dynamic obstacle-tracking problem solved with efficient parametric filters. The parametric filters make use of a new target representation and stable features developed specifically for tracking full-size vehicles in a dense traffic environment. The algorithm is validated in real time, both in controlled experiments with full-size robotic vehicles and on data collected at the 2007 Defense Advanced Research Projects Agency (DARPA) Urban Challenge.